| Project/Area Number |
16330114
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Sociology
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
MAEDA Tadahiko The Institute of Statistical Mathematics, Department of Data Science, Associate Professor, データ科学研究系, 助教授 (10247257)
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| Co-Investigator(Kenkyū-buntansha) |
SAKAMOTO Yoshiyuki The Institute of Statistical Mathematics, Department of Data Science, Professor, データ科学研究系, 教授 (50000211)
NAKAMURA Takashi The Institute of Statistical Mathematics, Department of Data Science, Professor, データ科学研究系, 教授 (20132699)
TSUCHIYA Takahiro The Institute of Statistical Mathematics, Department of Data Science, Associate Professor, データ科学研究系, 助教授 (00270413)
MATSUMOTO Wataru The Institute of Statistical Mathematics, Department of Data Science, Assistant Professor, データ科学研究系, 助手 (10390585)
HOSHINO Takahiro The University of Tokyo, Department of Cognitive and Behavioral Sciences, Assistant Professor, 大学院総合文化研究科, 講師 (20390586)
清水 信夫 統計数理研究所, 調査実験解析研究系, 助手 (00332130)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥15,400,000 (Direct Cost: ¥15,400,000)
Fiscal Year 2006: ¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2005: ¥10,100,000 (Direct Cost: ¥10,100,000)
Fiscal Year 2004: ¥3,600,000 (Direct Cost: ¥3,600,000)
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| Keywords | mode effect in surveys / covariate adjustment / non-probability sampling / random sampling / web survey / self-administration questionnaire / mail survey / propensity score |
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| Research Abstract |
The purpose of the present study was to propose a method for making a weight variable to adjust for the covariate distribution when mixed-mode surveys were administered simultaneously on different subsets of population. We also apply our method to real survey data to discuss its utility. Specifically we pay attention to comparison of survey by mail or face-to-face interview administered to probability samples and web survey administered on non-probability samples. We use the latter to approximate the former. The core of our method is estimating the propensity score, based on the framework of covariate adjustment.
As the result of our study, we found that in approximating the results of surveys on probability sample by results of survey done on non-probability samples by different mode, choosing good sets of covariate is very important. We proposed a four-step procedure of variable selection for better covariate adjustment. In addition, we also emphasized that obtaining results of high quality in web survey itself is another key for success in that approximation. We discussed various aspects of quality control in web survey administration. Based on these results, we will further investigate into the task of appropriately integrate the results of mixed-mode surveys into single estimates of population parameter.
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